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Decades of literature in traffic crash modeling show the popularity of generalized linear models (GLMs). However, because of the failure to accommodate spatial heterogeneity, parameters estimated with these models are inconsistent and inefficient. In light of that, this study aims to investigate the spatial heterogeneity of crashes aggregated at roadway segment levels using geographically weighted Poisson regression (GWPR) and two variants of the geographically weighted negative binomial regression (GWNBR) model. The results indicate: (i) the GWNBR model with global dispersion parameter outperforms conventional Poisson, GWPR, and negative binomial (NB) models; (ii) the performance of the GWNBR model further enhances as the dispersion parameter becomes spatially non-stationary; (iii) tests of spatial heterogeneity and autocorrelation reveal the existence of non-stationarity and less than 1% likelihood of randomness; and (iv) median of parameter estimates reveal a positive association between crashes and posted speed limit, number of lanes, number of three-leg intersections, number of access points, and vehicle miles traveled (VMT). The study concludes that, when spatial heterogeneity is evident, conventional GLMs should be avoided to circumvent the futile estimation of parameters on the arterial segments. The findings are expected to contribute to the small pool of literature on spatial non-stationarity of parameters in segment-based aggregation, identification and selection of segments with similar influence factors in corridor-level studies, and spatial analysis of arterial crashes.

Signal offset for coordinated traffic signal control is traditionally optimized based on posted speed limit, free-flow speed, or average speed among intersections, without considering the variations of travel speed. Variation in travel speed caused by interference on arterials may lead to inaccurate offset estimation, reducing the efficiency of coordination control. Therefore, this study develops an arterial offset optimization method for traffic signal coordination control using real-time speed collected from high-resolution crowdsourced data. The objective of the proposed method is to minimize the average delay on the corridor. The optimization problem is formulated as integer programming, and a genetic algorithm (GA) is utilized to search for the best offset solution. The proposed method is evaluated on a major arterial (Speedway Boulevard) in Tucson, Arizona. In the numerical exercise, the effectiveness and performance of the proposed method are evaluated in various scenarios, including a scenario with non-recurring congestion. The results show that using high-resolution real-time speed data can reduce travel delay time in a coordinated direction by 32.5% and 17.6% when compared with methods using speed limit and free-flow speed, respectively, and the proposed method is more reliable and robust for handling traffic conditions with varying volume and speed.

Unplanned events present significant challenges for operations and management in metro systems. Short-term ridership prediction can help agencies to better design contingency strategies under unplanned events. Though many short-term prediction methods have been proposed in the literature, most studies focused on typical situations or planned events. The study develops methods for the short-term metro ridership prediction under unplanned events. It explores event impact representation mechanisms and deals with the imbalanced data training problem in building the prediction model under unplanned events. Typical machine learning and deep learning methods are developed for exploration. A large-scale automatic fare collection (AFC) dataset and event record data for a heavily used metro system are used for empirical studies. The analysis found that the same type of unplanned event shares a similar and consistent demand change pattern (with respect to the demand under typical situations) at the station level. The synthetic minority oversampling technique (SMOTE) can enrich the ridership observations under unplanned events and generate a balanced dataset for model training. Given the occurrence of unplanned events, the results show that a combination of demand change ratio and the SMOTE oversampling technique enables the prediction models to learn the impact of unplanned events and improve the prediction accuracy under unplanned events. However, the oversampling methods (i.e., SMOTE and replication) slightly deteriorate the prediction accuracy for ridership under normal conditions. The findings provide insights into mechanisms for disruption impact representation and oversampling imbalanced data in model training, and guide the development of models for short-term prediction under unplanned events.

Distracted driving can pose great risks to road traffic safety. Although there is a rich body of literature devoted to identifying the statistical association between distracted driving and crash risks, few are available to examine the causal effect mechanism of distracted driving. Thus, the study attempts to conduct the causal mediation analysis to reveal the impact mechanism of distracted driving on crash injury risks, in which various hazardous driving actions are used as the mediators between driver distraction and crash injuries. Sensitivity analysis is also carried out to validate the underlying assumption of causal mediation analysis. The analytic results indicate that 1) distracted driving can lead to a higher likelihood of hazardous driving actions such as failing to yield, disobeying traffic control devices, driving left of lane center, and failing to stop in assured clear distance, 2) both the driver distraction and hazardous actions are the contributory factors to the severe crash injuries, and 3) distracted driving is identified to have significant mediation effects on crash injury risks. The study confirms the causal mediation effects of distracted driving on crash injury risks, which can serve to propose specific safety countermeasures to mitigate the crash injury risks.

Distracted driving is a critical factor affecting traffic safety. Although previous studies have been devoted to exploring the effects of driver distraction on crash risks, few of them are availabl...

Introduction: Quasi-induced exposure (QIE) technique has been popularly applied in the field of traffic safety research for decades. One of the basic assumptions of QIE theory is that the not-at-fault driving parties (D2s) involved in the crashes are the random selection of overall driving population at the event of crash occurrence. Very few literatures, however, can be identified to validate the assumption for crashes with specific injury severities that may not be satisfied in reality. Method: The study aims to check the validity of the assumption categorized by crash injury severity with the use of Michigan crash data. Latent class analysis is employed to generate several latent classes for the crashes with specific injury outcomes. Chi-square test is adopted to identify the significance of the similarity of D2 distributions among the latent classes. Results: The results indicate that: (a) for fatal crashes the statistical tests do not identify the significant discrepancies for D2 distributions of driver gender, age, and vehicle type between latent classes; (b) for injury crashes, both D2 driver gender and age have the similar distributions between/among various classes, while the D2 vehicle types show the inconsistent distributions; and (c) with respect to property damage only crashes, the distributions of three vehicle-driver characteristics are significantly different among the latent classes. It implies that the underlying assumption may not entirely hold true for all the injury severities and driver-vehicle characteristics. Practical Applications: The findings pinpoint the applicability of the QIE technique under specific scenarios and highlight the importance of validating the underlying assumption of QIE prior to its application.

oordinated signalization has been applied on arterials since 1960s. However, long-term effects of signal coordination on traffic safety have not been fully discussed, especially in terms of the tem...

Rationale: Endothelial injury may provoke emphysema, but molecular pathways of disease development require further discernment. Emphysematous lungs exhibit decreased expression of HIF-2α (hypoxia-inducible factor-2α)-regulated genes, and tobacco smoke decreases pulmonary HIF-2α concentrations. These findings suggest that decreased HIF-2α expression is important in the development of emphysema.Objectives: The objective of this study was to evaluate the roles of endothelial-cell (EC) HIF-2α in the pathogenesis of emphysema in mice.Methods: Mouse lungs were examined for emphysema after either the loss or the overexpression of EC Hif-2α. In addition, SU5416, a VEGFR2 inhibitor, was used to induce emphysema. Lungs were evaluated for HGF (hepatocyte growth factor), a protein involved in alveolar development and homeostasis. Lungs from patients with emphysema were measured for endothelial HIF-2α expression.Measurements and Main Results: EC Hif-2α deletion resulted in emphysema in association with fewer ECs and pericytes. After SU5416 exposure, EC Hif-2α-knockout mice developed more severe emphysema, whereas EC Hif-2α-overexpressing mice were protected. EC Hif-2α-knockout mice demonstrated lower levels of HGF. Human emphysema lung samples exhibited reduced EC HIF-2α expression.Conclusions: Here, we demonstrate a unique protective role for pulmonary endothelial HIF-2α and how decreased expression of this endogenous factor causes emphysema; its pivotal protective function is suggested by its ability to overcome VEGF antagonism. HIF-2α may maintain alveolar architecture by promoting vascular survival and associated HGF production. In summary, HIF-2α may be a key endogenous factor that prevents the development of emphysema, and its upregulation has the potential to foster lung health in at-risk patients.

Pathologic lymphatic remodeling in lymphedema evolves during periods of tissue inflammation and hypoxia through poorly defined processes. In human and mouse lymphedema, there is a significant increase of hypoxia inducible factor 1 α (HIF-1α), but a reduction of HIF-2α protein expression in lymphatic endothelial cells (LECs). We questioned whether dysregulated expression of these transcription factors contributes to disease pathogenesis and found that LEC-specific deletion of Hif2α exacerbated lymphedema pathology. Even without lymphatic vascular injury, the loss of LEC-specific Hif2α caused anatomic pathology and a functional decline in fetal and adult mice. These findings suggest that HIF-2α is an important mediator of lymphatic health. HIF-2α promoted protective phosphorylated TIE2 (p-TIE2) signaling in LECs, a process also replicated by upregulating TIE2 signaling through adenovirus-mediated angiopoietin-1 (Angpt1) gene therapy. Our study suggests that HIF-2α normally promotes healthy lymphatic homeostasis and raises the exciting possibility that restoring HIF-2α pathways in lymphedema could mitigate long-term pathology and disability.

Short-term metro passenger flow prediction is vital for the operation and management of metro systems. Most studies focus on the higher prediction accuracy with statistical and machine learning methods, but little attention has been paid to the prioritization and selection of feature variables, especially for different metro station types. This study aims to analyze the effect of feature variables on the prediction results, and then select appropriate predictor variables accordingly. A novel three-stage framework is proposed to prioritize feature variables for short-term metro passenger flow prediction, including station clustering, feature extraction, and variable prioritization. A hierarchical clustering algorithm (AHC) is developed for station clustering, the results of which are verified by the K-means and Davies-Bouldin (DB) statistical index. We then extract the temporal, spatial, and external features. Finally, the association between the variables and the prediction results is explored using tree-based models. The proposed framework is demonstrated and validated with data collected from Shanghai Metro Automatic Fare Collection (AFC) system. The results highlight that the importance of feature variables for developing models varies between stations, whereas only a few variables are found to explain most of the variation in the testing dataset; different feature variables lead to distinct differences in prediction accuracy, and simply adding more predictor variables does not necessarily lead to higher prediction accuracy. In addition, the station type and prediction type (i.e., tap-in and tap-out) have little influence on the selection of feature variables.

As a widely present form of bimodal transit systems, the trunk-feeder transit corridor has received less attention in the academic research compared to the conventional single-modal systems, partly due to the difficulty in the two-dimensional modeling. The schedule coordination of this corridor has been largely ignored, which can potentially strengthen the mutually reinforcing relationship between the trunk transit (e.g., rail) and the feeder transit (e.g., bus). In light of this, the paper proposes a novel two-stage programming model with the objective of minimizing the total cost to design a coordinated trunk-feeder corridor. In stage one, the continuous approximation approach and the discrete method are incorporated to obtain an optimized uncoordinated scheme and a feasible layout. The screened layout parameters such as the specific station location, feeder line location, length of the feeder line segments parallel to the trunk line, and the passenger flow distribution, are recruited into the coordination design in stage two. A nested two-phase optimization algorithm integrated with the analytic method and adaptive genetic algorithm is proposed to find the solutions to the mixed integer nonlinear program issue. Results show that the proposed method is suitable for heterogeneous schedule coordination design and the coordination strategy is more preferable at a lower-level heterogeneous demand. Meanwhile, different from the conventional approaches, this method can generate a rational cost-saving coordination scheme with a feasible layout under the two-dimensional heterogeneous demand pattern.

Accurate prediction of short-term passenger flow is vital for real-time operations control and management. Identifying passenger demand patterns and selecting appropriate methods are promising to improve prediction accuracy. This paper proposes a hybrid prediction model with time series decomposition and explores its performance for different types of passenger flows with varied characteristics in urban railway systems. The Seasonal and Trend decomposition using Loess (STL) is used to decompose the passenger flow into the seasonal, trend, and residual time series, representing the constant, long-term fluctuant, and stochastic passenger demand patterns. The approximate entropy (ApEn) is utilized to quantify the predictability of each component. After that, the Holt-Winters (HW) method is developed to predict the seasonal and trend components that both with high predictability. The long short-term memory networks (LSTM NN) are proposed to predict the residual with low predictability. The outputs from these models are combined to predict the short-term passenger flow. We assess the model performance with entry, exit, and transfer passenger flows at the target stations, which are selected via the agglomerative hierarchical clustering (AHC) algorithm in the Shanghai Metro. Compared with three representative models, the results show that 1) the decomposition-based hybrid model performs well for both one-step and multi-step predictions in terms of accuracy and robustness; 2) ApEn is useful for choosing appropriate prediction models.

Studies of spatial effects on road traffic safety are highly skewed in relation to aggregation units. As a result, the spatial effect of road traffic crashes at segment-level aggregation is rarely investigated. In light of that, this study investigates spatial spillover effects to determine the effects of independent variable changes initiated in one segment on the dependent variable of its neighboring segments. Additionally, the performance of the implemented models is evaluated under five different spatial weighting approaches. Spatial spillover effect of fatal and injury crashes aggregated at the segment level is estimated with the spatial lag of explanatory variables model under the general framework of Poisson and negative binomial models. The results indicate (i) spatial spillover effects are better modeled with spatial relation conceptualized by inverse distance spatial weighting schemes; (ii) spatial lag of explanatory variables under negative binomial and inverse distance spatial weighting provides the best model fitting; (iii) exogenous interactions among influence factors of fatal and injury crashes have a noteworthy effect in determining the crash frequency of neighboring segments. Convincingly, spatial spillover effect seems to significantly affect the results of conventional count modeling at the segment levels. To that effect, this study expects to provide empirical evidence and complement the existing literature on the impact of changes in explanatory variables initiated in a given segment on the safety of segments in proximity.

Crash hot spot identification and prediction using spatial statistics and random forest methods on the interstate of Michigan are evaluated. The Getis-Ord statistics are adopted to identify hot spots using location, frequency, and equivalent property damage only weights computed from the cost and severity of crashes. In the random forest approach, data patterns between 2010 and 2017 are determined to predict hot spots of crashes in 2018. Accordingly, the results indicate that: (i) interstate routes have witnessed 13,089 crashes on significant hot spots, 7,413 on cold spots, and the rest in other locations; (ii) random forest shows 76.7% and 74% accuracy for validation and prediction, respectively. The performance of the model is further affirmed with precision, recall, and F-scores of 75%, 74%, and 70%, respectively; and (iii) clustering of the crashes exhibits spatial dependence of high and low equivalent property damage only crashes. The practical significance of the approach is highlighted in the identification and prediction of hot spots.

The paper proposes a continuum approximation-based optimization model, considering vehicle emissions, to jointly optimize a bimodal transit network service in a heterogeneous environment where the passenger demand is not uniformly distributed over space and time. Correspondingly, the designed transit service characteristics, including the spacing of lines and stations, line headways and lengths, may vary over space and time to better cater for variable passenger demands. A successive substitution solution approach and an endpoint method are employed to generate an optimal solution. Experiments are conducted to illustrate the properties of the model and validate the solution methods. The results indicate that a trunk-feeder bimodal transit network is preferable under a higher-level heterogeneous demand. A rail-bus system is much more desirable compared to a bus rapid transit (BRT)-bus bimodal system in a larger and more developed city. Another interesting finding is that the scale of at-stop emission cost reduction is significantly larger than that of inter-stop emission. Compared to the conventional bimodal transit network design models without accounting for vehicle emissions, the incorporation of an emission factor into the optimization model can reduce both the total system and emission costs, which consequently achieves a more sustainable bimodal transit system.

Analyzing speed mean and variance is vital to safety management in urban roadway networks. However, modeling speed mean and variance on structured roads could be influenced by the spatial effects, which are rarely addressed in the existing studies. The inadequacy may lead to biased conclusions when considering vehicle speed as a surrogate safety measure. The current study focuses on developing a Bayesian modeling approach with three types of spatial effects, i.e., spatial correlation, spatial heterogeneity, and spillover effect. To capture the spatial correlation, the study employs the intrinsic conditional autoregressive (ICAR) models, spatial lag models (SLM), and spatial error models (SEM). Spatial heterogeneity and spillover effect are considered by the random parameters approach and spatially lagged covariates (SLCs). Speed data are collected from the float cars running on 134 urban arterials in Chengdu, China. The results indicate that the random parameters ICAR model with SLCs (RPICAR-SLC) outperforms others in terms of goodness-of-fit, accuracy, and efficiency for modeling speed mean, while the random parameters ICAR model (RPICAR) is the best for modeling speed variance. Moreover, RPICAR-SLC and RPICAR models are beneficial to address spatial correlation of residuals, explaining the unobserved influence among the observations, and are less likely to cause biased or overestimated parameters. The study also discusses how traffic conditions, road characteristics, traffic management strategies, and facilities on roadway networks influence speed mean and variance. The findings highlight the importance of multi-type spatial effects on modeling speed mean and variance along the structured roadways.

Pulmonary arterial hypertension (PAH) is a chronic, incurable condition characterized by pulmonary vascular remodeling, perivascular inflammation, and right heart failure. Regulatory T cells (Tregs) stave off autoimmunity, and there is increasing evidence for their compromised activity in the inflammatory milieu of PAH. Abnormal Treg function is strongly correlated with a predisposition to PAH in animals and patients. Athymic Treg-depleted rats treated with SU5416, an agent causing pulmonary vascular injury, develop PAH, which is prevented by infusing missing CD4+CD25highFOXP3+ Tregs. Abnormal Treg activity may also explain why PAH disproportionately affects women more than men. This mini review focuses on the role of Tregs in PAH with a special view to sexual dimorphism and the future promise of Treg therapy.

Introduction: Signal coordination has been wildly implemented on urban arterials to improve traffic efficiency. The impacts of signal coordination on traffic safety, however, are largely overlooked, particularly on crash propensities of driver–vehicle cohorts, which will vary due to changing traffic flow patterns. Method: The paper aims to compare crash risks of various driving cohorts (measured by relative crash involvement ratio) on arterials with and without signal coordination with quasi-induced exposure technique, which has been well developed in estimating crash risks for driver–vehicle characteristics (i.e., driver age, gender, and vehicle type). Michigan traffic crash data (2000–2014) are retrieved for the case study. Results: The results indicate that: (a) when signal coordination is implemented, young, male drivers, and pickups are associated with more crash responsibilities; (b) crash propensities vary for different disaggregated situations, e.g., young drivers may experience the rapid increase in crash risks during the peak hours; and (c) more hazardous actions (e.g., failing to stop in assured clear distance) are witnessed for the high-risk driving cohorts on the coordinated arterials than non-coordinated ones. Conclusions and practical applications: The findings highlight the importance of safety impact analysis of signal coordination, and serve to guide the potential improvements of safety operation and management of signal coordinated arterials.

Gefitinib, a selective epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor, has showed remarkable activity in non-small cell lung cancer patients with tumors carrying sensitive EGFR mutation. However, Iressa® tablets present problems, including insolubility, large absorption disparity, low oral bioavailability, gastrointestinal side effects and drug resistance, which limit their application. In present study, we developed gefitinib-loaded human serum albumin nanoparticles (GFNPs) by Nab™ technology for intravenous administration. By utilizing oleic acid as a co-emulsifier, which could increase the binding of gefitinib to human serum albumin, GFNPs were successfully fabricated with an amorphous state in GFNPs and its release from the nanoparticles conformed to the Ritger-Peppas process. The albumin nanoparticles could rapidly disassociate in the bloodstream and were effectively uptaken by the NCI–H358 cells via the mechanisms of secreted protein acidic and rich in cysteine (SPARC)-mediated transport, resulting in higher growth inhibition and apoptosis induction compared to free gefitinib. In tumor-bearing mice, the albumin nanoparticles demonstrated an increased, lasting tumor distribution. Compared with the commercial Iressa, GFNPs at only 10% of Iressa dose showed superior antitumor efficacy, with little effect on body weight of mice, suggesting that GFNPs might serve as a clinical candidate for a safe and effective anti-non-small cell lung cancer treatment.

Gene therapy represents a promising treatment for the Alzheimer׳s disease (AD). However, gene delivery specific to brain lesions through systemic administration remains big challenge. In our previous work, we have developed an siRNA nanocomplex able to be specifically delivered to the amyloid plaques through surface modification with both CGN peptide for the blood–brain barrier (BBB) penetration and QSH peptide for β-amyloid binding. But, whether the as-designed nanocomplex could indeed improve the gene accumulation in the impaired neuron cells and ameliorate AD-associated symptoms remains further study. Herein, we prepared the nanocomplexes with an siRNA against β-site amyloid precursor protein-cleaving enzyme 1 (BACE1), the rate-limiting enzyme of Aβ production, as the therapeutic siRNA of AD. The nanocomplexes exhibited high distribution in the Aβ deposits-enriched hippocampus, especially in the neurons near the amyloid plaques after intravenous administration. In APP/PS1 transgenic mice, the nanocomplexes down-regulated BACE1 in both mRNA and protein levels, as well as Aβ and amyloid plaques to the level of wild-type mice. Moreover, the nanocomplexes significantly increased the level of synaptophysin and rescued memory loss of the AD transgenic mice without hematological or histological toxicity. Taken together, this work presented direct evidences that the design of precise gene delivery to the AD lesions markedly improves the therapeutic outcome. KEY WORDS: siRNA delivery, Neurons, Amyloid plaques, BACE1 gene, Alzheimer׳s disease

Signal coordination has been popularly implemented at signalized intersections along the urban arterials. It can generate the green waves for vehicles to travel through the intersections with fewer...

Many cities have adopted signal coordination schemes to improve the operational efficiency of arterials. Through providing a “green wave”, the coordinated signals allow vehicles to pass consecutive intersections with fewer stops. Conventionally, studies focused mainly on the efficiency aspect of signal coordination. From a safety perspective, there are few studies devoted to investigating spatial heterogeneity and homogeneity simultaneously along arterials, particularly those with different traffic control and signal coordination schemes. Applying a joint negative binomial conditional auto-regression crash sum (JNBCS-CAR) model, this paper aims to comprehensively evaluate traffic safety on arterials with coordinated traffic signals. The performance of JNBCS-CAR is compared to those of traditional five ones (i.e., Poisson log-normal (PLN), negative binominal crash sum (NBCS), Poisson log-normal conditional auto-regression (PLN-CAR), negative binominal conditional auto-regression crash sum (NBCS-CAR), and hierarchical Poisson log-normal conditional auto-regression (HPLN-CAR)). Nine years of data are selected from the City of Ann Arbor (Michigan). Results indicate that (1) the JNBCS-CAR model outperforms other models in terms of the goodness-of-fit; (2) signalized intersections appear to have significant spatially heterogeneous effects on roadway segments under the scenario of signal coordination, while roadway segments do not have spatially heterogeneous effects on signalized intersections; and (3) there is a strong spatially homogeneous correlation among the coordinated intersections, but not among the segments. The findings serve to provide an accurate evaluation of the coordinated signalization from the safety perspective.

Background: Hypoxia-inducible factors (HIFs), especially HIF-1α and HIF-2α, are key mediators of the adaptive response to hypoxic stress and play essential roles in maintaining lung homeostasis. Human and animal genetics studies confirm that abnormal HIF correlates with pulmonary vascular pathology and chronic lung diseases, but it remains unclear whether endothelial cell HIF production is essential for microvascular health. The large airway has an ideal circulatory bed for evaluating histological changes and physiology in genetically modified rodents. Methods: The tracheal microvasculature of mice, with conditionally deleted or overexpressed HIF-1α or HIF-2α, was evaluated for anatomy, perfusion, and permeability. Angiogenic signaling studies assessed vascular changes attributable to dysregulated HIF expression. An orthotopic tracheal transplantation model further evaluated the contribution of individual HIF isoforms in airway endothelial cells. Results: The genetic deletion of Hif-2α but not Hif-1α caused tracheal endothelial cell apoptosis, diminished pericyte coverage, reduced vascular perfusion, defective barrier function, overlying epithelial abnormalities, and subepithelial fibrotic remodeling. HIF-2α promoted microvascular integrity in airways through endothelial angiopoietin-1/TIE2 signaling and Notch activity. In functional tracheal transplants, HIF-2α deficiency in airway donors accelerated graft microvascular loss, whereas HIF-2α or angiopoietin-1 overexpression prolonged transplant microvascular perfusion. Augmented endothelial HIF-2α in transplant donors promoted airway microvascular integrity and diminished alloimmune inflammation. Conclusions: Our findings reveal that the constitutive expression of endothelial HIF-2α is required for airway microvascular health.

Partial taxi speeders are observed with both high speeding frequency and severity (range). They thereby can be viewed as aggressive speeders whose behaviors may result in more hazards than others. Among the factors contributing to taxi speeding, the operational factors are proven to be deterministic. However, previous studies mainly investigate the operational factors of taxi speeding frequency, which fail to comprehensively unveil the impact of factors on speeders, especially for aggressive speeders. This study intends to disclose the operational factors affecting the aggressive taxi speeders with the random parameters Bayesian least absolute shrinkage and selection operator (LASSO) modeling approach. Taxi speeding behaviors and several operational factors are extracted from taxi GPS trajectory data in Chengdu, China. Based on the hourly speeding frequency and average speeding severity of each speeder, the fuzzy C-means clustering algorithm is employed to categorize taxi speeders into three cohorts: restrained speeder (RS), moderate speeder (MS), and belligerent speeder (BS). Compared to RS, MS and BS are treated as the aggressive taxi speeders. Several binary logistic models are developed with RS as the reference category. The random parameters Bayesian binary logistic LASSO model that captures the unobserved heterogeneity and tackles the multicollinearity is found to be the best fit model to identify the significant operational factors. The results indicate that aggressive taxi speeders are linked to longer daily driving distance and cruise distance, shorter delivery time, higher hourly income, driving at night, and driving on low-speed limit roads. However, intensive lane-changes and sufficient daily naps do not contribute to aggressive taxi speeders. Moreover, BS is more sensitive to the operational factors than MS. This study stresses the necessity of implementing speeder classification in taxi driver management and conceiving countermeasures considering the operational factors which are significantly associated with the aggressive taxi speeders.

Leukotrienes are biologically active eicosanoid lipid mediators that originate from oxidative metabolism of arachidonic acid. Biosynthesis of leukotrienes involves a set of soluble and membrane-bound enzymes that constitute a machinery complex primarily expressed by cells of myeloid origin. Leukotrienes and their synthetic enzymes are critical immune modulators for leukocyte migration. Increased concentrations of leukotrienes are implicated in a number of inflammatory disorders. More recent work indicates that leukotrienes may also interact with a variety of tissue cells, contributing to the low-grade inflammation of cardiovascular, neurodegenerative, and metabolic conditions, as well as that of cancer. Leukotriene signaling contributes to the active tumor microenvironment, promoting tumor growth and resistance to immunotherapy. This review summarizes recent insights into the intricate roles of leukotrienes in promoting tumor growth and metastasis through shaping the tumor microenvironment. The emerging possibilities for pharmacological targeting of leukotriene signaling in tumor metastasis are considered.

The quality of vehicular collision data is crucial for studying the relationship between injury severity and collision factors. Misclassified injury severity data in the crash dataset, however, may cause inaccurate parameter estimates and consequently lead to biased conclusions and poorly designed countermeasures. This is particularly true for imbalanced data where the number of samples in one class far outnumber the other. To improve the classification performance of the injury severity, the paper presents a robust noise filtering technique to deal with the mislabels in the imbalanced crash dataset using the advanced machine learning algorithms. We examine the state-of-the-art filtering algorithms, including Iterative Noise Filtering based on the Fusion of Classifiers (INFFC), Iterative Partitioning Filter (IPF), and Saturation Filter (SatF). In the case study of Cairo (Egypt), the empirical results show that: (1) the mislabels in crash data significantly influence the injury severity predictions, and (2) the proposed M-IPF filter outperforms its counterparts in terms of the effectiveness and efficiency in eliminating the mislabels in crash data. The test results demonstrate the efficacy of the M-IPF in handling the data noise and mitigating the impacts thereof.